Machine learning has generally been thought of and attempted to be implemented in the context of artificial intelligence. Artificial intelligence depends on algorithmic solutions (e.g., a computer program) to replicate particular human acts and/or behaviors. The study of neural networks is a sub-area of artificial intelligence which attempts to mimic certain human brain behavior by using individual processing elements that are interconnected by adjustable connections.
In human cognition, perception and understanding of phenomena happen over time and space. This perception is sometimes passive meaning that we observe a phenomena without acting on it in any way. However, the majority of sensed perception is at least partially based on our actions. For example, the actions we perform such as walking and moving our heads cause constant change in our visual environment which in turn causes us to perceive phenomena from different angles and perspectives. Actions are also fundamental in our learning process. When a human encounters a new object or phenomena for the first time, the human may subject the object to a series of actions in order to “understand” the object. For instance, a child seeing a new toy for the first time may pick up the toy and rotate the toy around to perceive it from all angles.
Hierarchical Temporary Memories (HTMs) have been developed to simulate temporal aspects of perception and learning. An HTM is a hierarchical network of interconnected nodes that individually and collectively (i) learn, over space and time, one or more causes of sensed input data and (ii) determine, dependent on learned causes, likely causes of novel sensed input data. While determining causes of sensed input data is a powerful use of HTMs, this model fails to consider the actions governing the sequences of sensed inputs.